Level-grade crossings, the intersections of railroad railways with vehicle roadways and pedestrian walkways, present safety problems. Moving railroad trains possess a large amount of inertia and usually require large amounts of travel distance to be brought to a stop. Disabled vehicles on the rail tracks and other stationary objects abandoned on the level crossings pose serious traffic safety threats, and accidents caused by collisions of moving railroad trains with such objects frequently result in significant loss of life and property. Typically, by the time a train engineer recognizes that an object on a level crossing is static and will collide with the moving train, it is too late to take action and bring the train to a stop to avoid the collision.
Accordingly, a variety of automated alarm systems are proposed or implemented in order to determine collision hazards caused by static objects located within such level-grade crossings. Such systems may provide warnings to train operators or other parties remote from the crossing that are early enough to enable avoidance measures, such as sending personnel to the crossing to remove the object, or notifying a train engineer to begin stopping the train early enough so that the train will stop prior to entry of the crossing. However, such early warning systems suffer from a variety of limitations. For example, the capabilities of such systems may be limited by reliance on human perception to review video feeds of crossings and make the necessary determinations to spot and abate problems. The number of personnel available to watch video footage from vast camera arrays is generally limited by budgetary and other resource limitations, as is the ability of any one human monitor to perceive a threat in a given video feed. The process of watching surveillance videos is resource consuming, suffers from high costs of employing security personnel, and efficiency in such systems to detect events of interest is also limited by the constraints of human comprehension.
The field of intelligent visual surveillance seeks to address this problem by applying computer vision techniques to automatically detect specific static objects present on the railway and visible in the video streams. However, the efficacy of such systems in real-world conditions is limited, and high rates of false positive detections or low rates of accuracy in detecting true events may limit the usefulness and trustworthiness of such systems.